Datasets sources

This 3rd release of ReMap present the analysis of 5,798 quality controlled ChIP-seq (n=5,590) and ChIP-exo (n=208) datasets from public sources (GEO, ArrayExpress, ENCODE). Those ChIP-seq/exo datasets have been mapped to the GRCh38/hg38 human assembly. Here we define a “dataset” as a ChIP-seq experiment in a given series (e.g. GSE46237), for a given TF (e.g. NR2C2), in a particular biological condition (i.e. cell line, tissue type, disease state or experimental conditions ; e.g. HELA). Datasets were labeled by concatenating these three pieces of information such as GSE46237.NR2C2.HELA.



Datasets (QC pass) 5,590 208
Targets 927 208
Peaks 163,741,896 990,476


Transcriptionnal regulators

Search for specific factors


Cell lines and tissues

Search for specific cells


Quality controled ChIP-seq datasets

Browse a given dataset

165 million

Binding regions

Download our data

Integration of ChIP-seq and ChIP-exo data

In this ReMap 2020 human release we have manually curated and annotated 6,498 ChIP-seq experiments, retained after quality control 5,798 datasets and, for the first time, we included one large ChIP-exo experiment GSE78099 from Imbeault M. et al Nature 2017. We applied our pipeline for both type of data, however most postprocessing steps applied by Imbeault M. et al Nature 2017 where applied to the ChIP-exo data.

After consistent peak calling, we identified a total of 164.7 million peaks bound by transcriptionnal regulators (including 990,476 peaks from ChIP-exo). These numbers include overlapping sites for identical TRs which were studied in various conditions. To address this we merged overlapping TR peaks for similar TR obtaining a catalog of 75 million non-redundant peaks.

Datasets quality assessment

As not every ChIP-seq datasets are equal in terms of quality, we used four different metrics based on ENCODE ChIP-seq guidelines to retain high quality datasets for downstream analyses. First we used the normalized strand cross-correlation coefficient (NSC) which is a normalized ratio between the fragment-length cross-correlation peak and the background cross-correlation, and the relative strand cross-correlation coefficient (RSC), a ratio between the fragment-length peak and the read-length peak to exclude low quality datasets. We also used the fraction of reads in peaks (FRiP) and the number of peaks identified in each dataset to filter datasets.

Dataset(s) are plotted in a 2D vizualization with NSC and RSC as x- and y-axis, colours highlight the datasets conserved (green) or excluded (red) from the catalogue of binding sites.

Datasets quality plot


ChIP-exo post-processing

For the ChIP-exo data we applied three post-processing steps as described in Imbeault M. et al Nature 2017 material and methods. In brief, we filtered out peaks that would meet any of these criteria: MACS score <80 (equivalent to a P = 1 × 10−8); ratio of forward versus reverse strand reads greater than 4; fewer than 20 reads over 500 bp per 15 million reads; normalized read count was less than twofold over the control.

Annotation and classification of transcription factors

Function and description of transcriptionnal regulators present in this catalog (GEO, ArrayExpess, ENCODE) were retrieved from HGNC, Ensembl and RefSeq databases. When possible each transcription factor was also annotated using the classification of human transcription factors allowing users to filter specific TFs based on the characteristics of their DNA-binding domains.

Genomic visualization of peaks and analyses

To perform a de novo motifs analysis for each TF present in our catalogue, we provide a link to the Regulatory Sequence Analysis Tools.

A link to the UCSC Genome Browser was also added to facilitate genomic integration of the binding sites with other genome annotations. Our BED tracks allow for the visualization of our catalogues of binding sites on the human genome. Finally, different analyses such as the quality of datasets and DNA constraint analysis are provided for each transcription factor.

Downloading peaks

The ReMap BED files are available to download either for a given transcriptional regulator, by Biotype or for the entire catalog as one very large BED file.

For Homo sapiens the GRCh38/hg38 assembly is currently the supported assembly, but our files can be lifted to hg19 with liftover. We also provide an archive of the ReMap 2018 and 2015 catalogs.

For Arabidopsis thaliana we provide BED files for transcriptional regulators, histones marks, ecotypes and biotype coupled with a given ecotype. The TAIR10 assembly is the only assembly supported by ReMap.